Abstract Machine learning (ML) is an artificial intelligence technology that has been used in
atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles …

Properties of spatially explicit data are often ignored or inadequately handled in machine
learning for spatial domains of application. At the same time, resources that would identify …

Understanding population density at a fine spatial scale is beneficial for urban management
and planning. Existing machine learning methods have been widely used to predict the …

Data are increasingly spatio‐temporal—they are collected some‐where and at some‐time.
The role of proximity in spatial process is well understood, but its value is much more …

For buyers, investors and urban policy, understanding drivers of community-level house
prices across space and across time, are important for urban management and economic …

Conventional methods of machine learning have been widely used to generate spatial
prediction models because such methods can adaptively learn the map** relationships …

Multiscale geographically weighted regression (MGWR) is an important method that is used
across many disciplines for exploring spatial heterogeneity and modeling local spatial …

Models for estimating urban rental house prices in the real estate market continue to pose a
challenging problem due to the insufficiency of algorithms and comprehensive perspectives …

Accurate prediction of mineral resources is imperative to meet the energy demands of
modern society. Nonetheless, this task is often difficult due to estimation bias and limited …

Prediction of drought class transitions has been received increasing interest in the field of
water resource management. Markov chain models are effective prediction tools that are …